Soft sensor modeling based on DICA-SVR

Abstract

A new feature extraction method, called dynamic independent component analysis (DICA), is proposed in this paper. This method is able to extract the major dynamic features from the process, and to find statistically independent components from auto- and cross-correlated inputs. To deal with the regression estimation, we combine DICA with support vector regression (SVR) to construct multi-layer support vector regression. The first layer is feature extraction that has the advantages of robust performance and reduction of analysis complexity. The second layer is the SVR that makes the regression estimation. This kind of soft-sensor estimator was applied to estimation of process compositions in the simulation benchmark of the Tennessee Eastman (TE) plant. The simulation results clearly showed that the estimator by feature extraction using DICA can perform better than that without feature extraction and with other statistical methods for feature extraction. © Springer-Verlag Berlin Heidelberg 2005.